Three-dimensional map generation with metadata overlay for visualizing projected workflow impact in computing environment

ABSTRACT

In a computing environment comprising a plurality of equipment racks wherein each equipment rack comprises one or more of compute, storage and network assets, a method identifies a workflow attributed to at least a portion of the assets in at least one equipment rack of the computing environment, generates one or more overlays that visualize a projected impact to one or more components of the identified workflow, obtains a three-dimensional representation of the at least one equipment rack, and superimposes the one or more overlays on the three-dimensional representation of the at least one equipment rack.

FIELD

The field relates generally to computing environments, and moreparticularly to techniques for generating metadata overlays on maps ofsuch computing environments.

BACKGROUND

Data centers are computing environments that include compute, storageand network resources (assets) arranged in multiple rack-mountedenclosures (equipment racks or, simply, racks) placed at variouslocations within a physical space. Each rack may contain one or more ofthe compute, storage and networking assets, and the racks and theirassets collectively constitute the data center. In some scenarios, adata center includes a cloud computing platform, where “cloud” refers toa collective computing infrastructure that implements a cloud computingparadigm. For example, cloud computing is a model for enablingubiquitous, convenient, on-demand network access to a shared pool ofconfigurable resources (e.g., compute, storage, network assets) that canbe rapidly provisioned and released with minimal management effort orservice provider interaction. The dynamic provisioning andinterconnection of the various assets is accomplished while the variousassets remain positioned in their respective racks. However, whenphysical infrastructure in a given data center has to be powered off orremoved due to regular maintenance or failure, it is a significantchallenge for data center personnel to know whether such actions willhave a negative impact on software programs (e.g., applications) runningon the data center.

SUMMARY

Embodiments of the invention provide techniques for generating one ormore metadata overlays on a three-dimensional representation of acomputing environment such as, for example, a three-dimensional map of adata center. The one or more metadata overlays visually represent aprojected workflow impact associated with performing an action in thedata center.

For example, in one embodiment, a method comprises the following steps.In a computing environment comprising a plurality of equipment rackswherein each equipment rack comprises one or more of compute, storageand network assets, a method identifies a workflow attributed to atleast a portion of the assets in at least one equipment rack of thecomputing environment, generates one or more overlays that visualize aprojected impact to one or more components of the identified workflow,obtains a three-dimensional representation of the at least one equipmentrack, and superimposes the one or more overlays on the three-dimensionalrepresentation of the at least one equipment rack.

Advantageously, illustrative embodiments generate one or more metadataoverlays on a dynamic three-dimensional map of a data center, whereinthe metadata overlays provide a representation of end-to-end workflowimpact prior to certain actions (e.g., infrastructure power down,failure, replacement, etc.) occurring in the data center.

These and other features and advantages of the invention will becomemore readily apparent from the accompanying drawings and the followingdetailed description.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram of an architecture for generating metadataoverlays on maps of a computing environment, according to an embodimentof the invention.

FIG. 2 is a block diagram of an architecture for a map generatingservice for a computing environment, according to an embodiment of theinvention.

FIG. 3 is a block diagram of a topology management service for acomputing environment, according to an embodiment of the invention.

FIGS. 4A-4I illustrate generation of a three-dimensional map of acomputing environment and associated workflow impact overlays, accordingto an embodiment of the invention.

FIGS. 5A-5C illustrate a methodology for generation of athree-dimensional map of a computing environment and associated workflowimpact overlays, according to an embodiment of the invention.

FIG. 6 illustrates a processing platform used to implement anarchitecture for generating workflow impact overlays on maps ofcomputing environments, according to an embodiment of the invention.

DETAILED DESCRIPTION

Illustrative embodiments may be described herein with reference toexemplary computing environments, cloud infrastructure, datarepositories, data centers, data processing systems, computing systems,data storage systems and associated servers, computers, storage unitsand devices and other processing and computing devices. It is to beappreciated, however, that embodiments of the invention are notrestricted to use with the particular illustrative system and deviceconfigurations shown. Moreover, the phrases “cloud environment,” “cloudcomputing platform,” “cloud infrastructure,” “data repository,” “datacenter,” “data processing system,” “computing system,” “data storagesystem,” “computing environment,” and the like as used herein areintended to be broadly construed, so as to encompass, for example,private and/or public cloud computing or storage systems, as well asother types of systems comprising distributed virtual infrastructure.However, a given embodiment may more generally comprise any arrangementof one or more processing devices.

As mentioned above in the background section, when physicalinfrastructure in a data center has to be powered off or removed due toregular maintenance or failure, or some other infrastructure-affectingaction has to be performed, data center personnel (e.g., ITadministrators) are hesitant to make changes to the infrastructure orinitiate automated actions without being able to understand the impactsof the changes. Examples of initiating automated actions include, butare not limited to, software proactively migrating workloads based on astate setting chage, or a hypervisor or other virtualization managerinitiating a shutdown request based on the IT administrator setting anasset state change. More particularly, when managing infrastructure,prior to turning off a server or removing a drive, IT administratorswould like to make sure that their actions will not have a negativeimpact on workflows involving business critical applications.

While some of the many individual infrastructure assets in the datacenter may have dedicated software interfaces that enable a user torespectively log in and try to obtain a projected impact assessment, onemain drawback is that there is no ability for the user to obtain andconsume projected impact information in real-time while the user isphysically standing in the data center by the equipment that is to beshut down and/or replaced.

Illustrative embodiments overcome the above and other drawbacks byproviding a solution that generates an on demand visualization (one ormore metadata overlays over an image of a data center) of workflowassociated with one or more assets in the data center. In illustrativeembodiments, the visualization is available on a handheld device of anIT administrator (e.g., smart phone, tablet, etc.) while the user isstanding or otherwise physically located in the data center. With such asolution, the IT administrator can understand the consequences of theiractions in a user-friendly way before they take the actions. Themetadata overlay of projected impacts and behavior metadata (inclusiveof, but not limited to, metadata reflecting platform, application,workload, virtual machines, container, pods, databases, etc.) provides avisual representation of end-to-end workflow prior to the actionsoccurring. By way of example only, illustrative embodiments enable thevisualization of predicted/projected movement of virtual machines, pods,or databases projected onto a representation of one or more equipmentracks of a data center. Such a representation, in various embodiments,can be a real-time image of the equipment racks (augmented reality (AR)or virtual reality (VR) environment) and/or a stored image of theequipment racks. The visual representation of the racks with metadataoverlays can be presented on smart devices, mobile AR/VR interfaces, oreven the wall of the data center itself. Illustrative embodimentsfurther enable intelligent decision-making based on proposed changesduring hands-on infrastructure management using smart devices inclusiveof, but not limited to, watches, phones, tablets, headsets, projectiontechnologies, and other AR/VR devices.

A “workflow,” as illustratively used herein, refers to a set of one ormore software-related tasks in a data center that are performed toprovide a particular data center service or process.

A “pod,” as illustratively used herein, refers to a point of deliverymodule of network, compute, storage, and application components thatoperate together to deliver one or more services or processes in a datacenter.

More particularly, illustrative embodiments enable a user to view athree-dimensional topology overlay of metadata available within the datacenter and the potential projected impact of making changes on thephysical infrastructure grouped by known, useful sets, inclusive of butnot limited to, hardware utilization, platforms, applications,communication paths, etc.

For example, based on an application topology, a data visualization withoverlays can be projected in real-time on racks, rack contents, and allassets. By utilizing secondary data within existing platform andapplication management technologies, illustrative embodiments enablephysical visualization of projected asset utilization and componentmovement over time with the use of a smart device.

That is, standing at any point in the data center, the user is able topoint their smart device at a rack. They can then choose the action theywould like to take (e.g., power down a server) and illustrativeembodiments overlay the metadata visualization of the projected impacton the data center and workloads. Secondary data sources provide anindication of the asset to be investigated, and a map is displayed tothe in-house data center administrator.

Advantageously, illustrative embodiments provide utilization of AR andmetadata overlays to show projections of potential impacts on topologybased on selected behaviors utilizing secondary source data.Illustrative embodiments can be utilized by data center administratorsin real-time to show what would happen to applications should theychoose to perform actions against the infrastructure, and utilizeslocation-aware display technologies eliminating time-intensive loginsthrough a prohibitive number of management portals, navigation andlaptop access requirements.

As will be further explained, indoor positioning technologies broadcastobject location, with membership specification supplying the objecttype. Smart devices can determine the broadcast object which is closestto the device relative to other devices. For example, a user can standin front of rack 1, aisle 2, data center 3, and see via thevisualization including metadata overlaid on the three-dimensional mappresented on his/her smart device that this particular rack is runningten servers. On server 3, the user is informed via the visualizationthat server 3 is running a specific hypervisor, and that the server ispart of a cluster also including server 4. Further, the user is informedvia the visualization about the ten virtual machines (VMs) that arecurrently running on both of these servers and the applications to whichthey are tied.

Turning now to FIG. 1, an architecture 100 for generating one or moremetadata overlays on a map of a computing environment is depicted,according to an illustrative embodiment. As shown, a user 101 is locatedin a data center in the vicinity of a set of equipment racks 120 of thedata center, wherein each equipment rack 122-1, 122-2, 122-3 and 122-4comprises a plurality of assets 124 (e.g., compute, storage and/ornetwork assets). A data center 3D map with metadata overlays 130 isgenerated and presented to the user 101 via a VR controller interface102 coupled to a resource controller 104, which itself includes aninterface mapping module 106 and a data center workflow module 108,coupled to a 3D mapping service 110, secondary source(s) 112, a topologyservice 114, one or more platform interfaces 116 and one or more clusterinterfaces 118.

In this example, it is assumed that one of the assets in rack 122-1 andone of the assets in rack 122-2 currently host components provisionedfor one or more applications of Tenant A, i.e., POD1 and POD2 arerunning on the bottom asset 124 positioned in rack 122-1, while VM1 andVM2 are running on the bottom asset 124 positioned in rack 122-2.Further, it is assumed that one of the other assets in rack 122-2 andone of the assets in rack 122-3 currently host components provisionedfor one or more applications of Tenant B, i.e., DB1 is running on themiddle asset 124 positioned in rack 122-2, while DB2 is running on themiddle asset 124 positioned in rack 122-3. Lastly, it is assumed thatassets in rack 122-4 currently host components provisioned for one ormore applications of Tenant C, i.e., VM3, VM4 and VM5 are respectivelyrunning on the three assets 124 positioned in rack 122-4. It is to beappreciated that the numbers of racks and assets depicted in FIG. 1 arefor illustration purposes only and that a computing environment in whichillustrative embodiments are implemented may alternatively include moreor less racks and assets, more or less components, and differentcomponents than the ones shown.

Effectively by combining infrastructure information obtained from theset of racks 120 through platform interfaces 116 and workflowinformation obtained from components running on the assets throughcluster interfaces 118, the interface mapping module 106 and data centerworkflow module 108 of resource controller 104 utilize the 3D mappingservice 110, secondary source(s) 112 and topology service 114 togenerate the data center 3D map with metadata overlays 130. As will befurther explained below, the metadata overlays of the visualization(130) presented to user 101 reflect a projected impact to the variouscomponents (POD1, POD2, DB1, DB2, and VM1 through VM5) that a giveninfrastructure action (e.g., asset shutdown/replacement) would cause.This projected impact would be used by the user and/or other data centerpersonnel to decide whether or not to take that action or to alter theaction.

One or more illustrative embodiments include technologies and approachesas follows:

Application Definition: ability to correlate disparate workloads andassets to form “application definitions.” This is a known process whichutilizes a combination of labels/tags and network traffic patterns tocorrelate disparate workloads, VMs, etc., as members of a namedapplication across platforms/clouds/and bare metal. This enables thecorrelation of workloads to each other for purposes of visualization.

Platform Definition: ability to correlate all physical componentsutilized by a platform (e.g., VMware cluster, Kubernetes Nodes, AWScluster). This enables the visualization of impacted platforms.

Ownership definition: ability to correlate access rights to a platform,or subcomponents of a platform with domain names or other metadata tags.This is utilized by application impact projection technologies used indata center planning and management technologies such as VMotion. Thisenables the projection of locations which workloads would move to basedon changes in infrastructure.

Network traffic monitoring and mapping: ability to trace access pointsand correlate them to and using secondary data sets, correlate IPaddresses, correlating to physical hardware locations. This enables pathprojections in the instance of workload movement.

Asset impact projection: ability for platforms to project potentialimpact of changes across managed assets.

In one illustrative embodiment, 3D mapping service 110 generatesthree-dimensional map of a data center. That is, FIG. 2 depicts anarchitecture for a dynamic map generating service for a computingenvironment (e.g., 110), according to an embodiment of the invention. Bycombining several functionalities, illustrative embodiments enable thegeneration of a 3D map of a data center by standing in a single locationwith a smart device. Such functionalities include, but are not limitedto the following:

(i) Smart devices can determine measurements of objects based on animage utilizing relative sizing of known elements. These techniques arereadily available in application programs and through smart calculationsbased on known object size and relative dimension extrusion.

(ii) Indoor positioning technologies can broadcast object location, withmembership specification supplying the object type. Smart devices candetermine the broadcast object which is closest to the device relativeto other devices.

(iii) Modern servers/arrays and data center physical assets aretypically shipped and tracked with broadcasting/beacon technology(inclusive of, but not limited to, Bluetooth, RFID, and otherbroadcasting technologies) as part of manufacturing practices. Thesebeacons have a unique identifier enabling correlation of one signal toone asset.

Thus, as will be further explained herein, illustrative embodimentsperform correlation of the location where an image was taken, withrelative location of received indoor positioning technologies andcombined with a gossip-based mesh network map out an entire data centerby standing in a single location. The map is 3D in that each rack andasset is rendered in X, Y, Z coordinates (e.g., length, width, heightdimensions). An example of building a 3D map using the techniquesdescribed herein will be described in further detail below with respectto FIGS. 4A-4E.

Illustrative embodiments enable a user to near-instantly generate anentire 3D map of their data center, inclusive of racks, rack contents,and all assets. Illustrative embodiments also enable ongoing tracking ofassets and components over time. These features leverage advancements inpeer discovery, indoor positioning systems, broadcast networks, andimage recognition and manipulation on smart devices. Examples of smartdevices include, but are not limited to, smart phones, tablets, laptops,smart headsets, smart glasses, etc.

For example, standing at any point in the data center, the user is ableto point their smart device camera at a rack or row of racks. The smartdevice takes a single still-frame moment in time of the assets and usinga known object in the picture accurately determines the physicalcharacteristics of the closest rack (inclusive of width and height,proximity to racks next to it and distance from other objects). Thisdefines what will be used as Data Center Position (0,0,0) of Rack0(including length, width, height).

The same smart device taking that photo also acts as a receiver ofindoor positioning broadcast data. Each asset in the data center isequipped with a positioning beacon (e.g., Bluetooth, RFID, or otherbroadcasting technologies). Utilizing the received signal strength (RSS)from these beacon signals and secondary data source(s), illustrativeembodiments automatically determine the closest asset, defined as Asset0in Rack0. Correlation with secondary data enables the population of theexact device serial number and physical characteristics.

To determine the remaining contents of the rack, illustrativeembodiments leverage peer discovery (i.e., the ability of a device tofind other similar devices that are in the immediate physical vicinity,i.e., proximity) via a gossip-protocol overlay on top of a mesh network.By having each device serve as a sensor, an observed neighbor model isbuilt with RSS for each neighboring node. When combined with an Angle ofArrival (AoA) implementation, this provides relative proximity in the X,Y, and Z axis. This provides a build-out of relative location of thecontents of the first rack.

A gossip-based network is a distributed network of peer devices (inillustrative embodiments, assets with beacons) that is configured toenable peer-to-peer communication that is based on the way gossip isspread. That is, the distributed network uses peer-to-peer transfer ofdata to ensure that the data is routed to all members of the ad-hocnetwork. In some cases, the ad-hoc network is a mesh network. A meshnetwork is a network topology in which the devices connect directly,dynamically and non-hierarchically to as many other devices as possibleand cooperate with one another to efficiently route data therebetween.AoA measurement is a method for determining the direction of propagationof a signal or determined from signal strength. In some examples, AoAdetermines the direction by measuring the time difference of arrival andfrom these delays the AoA can be calculated.

The smart device then near-instantly provides a 3D rendering of theclosest rack and its contents. This 3D rendering can then be expandedwithin the mesh overlay to build out a model of the remaining assets inthe data center. In some scenarios, the first rack build-out onlyfocuses on Y axis relative location data (“higher” or “lower”) but withX axis proximity and the smart device measurement data, inference ofassets in adjacent racks can be discovered. This process is repeateduntil all assets in the data center are discovered and modeled.

With secondary data checks on the assets, serial data association canthen be performed to ensure that asset sizes are in line with width andheight expectations of each asset (e.g., 1 U versus 4 U servers). Whencombined with the RSS, it can then be inferred from minimal user inputwhat relative rack-unit each asset is located in. In some embodiments,the minimal user input requires a user to provide the location of atleast one “anchor” asset. Given the anchor asset, the localitycapabilities described herein may be used to determine the relativelocation of other assets. The anchor asset or assets may be any randomsampled assets. Thus, the minimal user input may include providing anindication that at least one random asset is in a particular location.In some embodiments, the location may be defined in terms of an “actualrack” and “rack-unit” where the actual rack refers to a datacenter rackcabinet giving a relative position in an overall room (e.g., “x” and “y”axis coordinates) and the rack-unit refers to the position within thedatacenter rack cabinet (e.g., a “z” axis coordinate). The user wouldonly need to provide the actual rack and rack-unit of additional randomsampled assets to start getting more and more accurate mapping. Theresult is a model of all assets in the data center that can then beprovided to a 3D rendering service that maps the data center.

Advantageously, a single location visual indicator on a smart devicecombined with proximity and a gossip-based mesh network is able togenerate a data center map, inclusive of rack layouts and secondary datasources. Illustrative embodiments utilize a smart device to correlate asingle physical location with object measurements, a 0,0,0 (X, Y, Z)located beacon and combined with a mesh broadcasting network ofthree-dimensional beacon data to map an entire data center and itscontents standing in one spot. Further, with illustrative embodiments,broadcast mesh network continuous topology updates are available. Thatis, utilizing illustrative embodiments, it is possible to track thechange in signals over time. Movement of physical assets are correlatedto changes in nearest-neighbors. Removal, or physical asset failures,can be correlated to beacon silence. The ongoing topology updates canthen be mapped in relation to the data center anchor point (Rack0,Asset0, Pos0).

Turning now to FIG. 2, an architecture is depicted for generatingdynamic 3D maps of data centers, in accordance with illustrativeembodiments as described herein. Note that the map generatingarchitecture in FIG. 2 depicts an exemplary data center similar to thedata center in FIG. 1. Thus, it can be assumed that any description andcomponents mentioned in the context of the data center in FIG. 2 arepresent in illustrative embodiments of the data center in FIG. 1. Forexample, assets 124 in FIG. 1 are similarly configured as assets 206 tobe discussed below in the context of FIG. 2, i.e., assets 124 havepositioning beacons operatively coupled to form a gossip-based meshnetwork.

As shown, a data center 201 comprises two equipment racks 202-1 and202-2. It is to be appreciated that this number of racks is forillustrative purposes and typical data centers with which map generationin accordance with illustrative embodiments is performed include morethan two equipment racks. Each rack comprises a plurality of assets 204(compute, storage, network assets) and each asset 204 has a positioningbeacon 206 installed thereon or therein. The positioning beacons 206 arepart of (peers in) a gossip-based mesh network 210 which is alsooperatively coupled to a smart device 220 operated by a user (e.g., user101 in FIG. 1). Smart device 220 includes an image capture module 222(e.g., cellular phone camera), a signal strength interpreter 224 and anobserved neighbor model 226. The smart device 220 is operatively coupledto a 3D rendering service 230 which is operatively coupled to asecondary data source(s) 232. The 3D rendering service generates a datacenter 3D map 234 (e.g., part of the visualization 130 depicted in FIG.1).

As mentioned above, standing at any point in the data center 201, theuser points image capture module 222 of smart device camera 220 at rack202-1 or 202-2 (in some embodiments, the camera can be pointed at bothracks 202-1 and 202-2). The image capture module 222 takes a singlestill-frame image of the rack (202-1 or 202-2) and its assets 204. Usinga “known object” in the captured image accurately determines thephysical characteristics (inclusive of width and height, proximity toracks next to it and distance from other objects) of the closest rack towhich the user is positioned. As explained, this location defines whatwill be used as Data Center Position (0,0,0) of Rack0 (including length,width, height).

The “known object” may be a server in a rack. The smart device 220participates in the gossip-based mesh network 210, and thereforeunderstands its relative distance to the server or other known objectwithin the rack. Using that distance estimation (e.g., from the smartdevice 220 to the server or other known object), RSS and thecharacteristics of the server or other known object (e.g., the size ofthe server) allows for determining the physical characteristics of theclosest rack. For example, from the single still-frame image of the rackand the RSS to the server or other known object, it can be determinedthat the smart device 220 is 4 feet (ft) from the server or other knownobject at a 27 degree angle. It is assumed that physical characteristicssuch as the size of the server or other known object are available(e.g., that the server is 1 U (1 RU), meaning that it is 1.25 inchestall and 19 or 23 inches wide depending on the rack). The size and otherphysical characteristics of the server or other known object may beverified by using a serial number look-up to obtain model details forthe server or other known object. Based on pixel measurements and takinginto account the angles from an internal measurement unit of the smartdevice 220, the size of the closest rack may be inferred (e.g., as thereare nearly 13 data points available for assessing the size of assets inthe image).

Further, signal strength interpreter 224 of smart device 220 acts as areceiver of indoor positioning broadcast data from the positioningbeacons 206 that are part of the gossip-based mesh network 210. Based ona comparison of the RSS values of the received beacon signals, signalstrength interpreter 224 determines the closest asset to the smartdevice 220. As mentioned, in some embodiments, secondary data fromsource(s) 232 enables the population of the exact device serial numberand physical characteristics of the closest asset. For example, once thesmart device 220 determines what it considers a closest asset, secondarydata such as a serial number and physical dimensions of the asset can beretrieved from an equipment database (source 232) that maintains thatinformation.

To determine the remaining contents of the rack with the closest asset,the smart device 220 leverages peer discovery via a gossip-based meshnetwork 210. An observed neighbor model 226 is built with RSS for eachneighboring node. A “neighboring node” refers to other assetsparticipating in the gossip-based mesh network 110. When combined withan Angle of Arrival (AoA) implementation, this provides relativeproximity in the X, Y, and Z axis. In some embodiments, it is assumedthat each asset or neighboring node has an associated neighbor tablewith RSS and AoA information. Multiple assets may be queried for theirassociated neighbor tables to perform an overlay. Consider, for example,two antenna arrays (associated with two assets) set perpendicular toeach other in Euclidean space. Each antenna array performs asimultaneous phase shift based calculation of θ=sin⁻¹(λϕ/2πd), where θis the AoA, A is the wavelength, ϕ is the phase shift between twoantennas, π is Pi, and d is the distance between the two antennas. Thephase shift-based calculations of θ may be used to give a 3D vector,which should be phase shifted based on internal measurement readings ofthe smart device 220 (e.g., readings of a gyroscope of the smart device220). The servers or other assets are assumed to be equipped similarlyfor performing the phase shift calculations. The assets, however, arenot required to include a gyroscope in cases where the standardorientation of the asset is static (e.g., a server in a rack). Each ofthe assets can then store a direction cosine, standardizing therelations to a relative angle. Thus, a request between two endpoints orassets allows for an indication of relative positioning (e.g., “I am 3feet in front, 2 feet up, and 1 foot left of you”) to be communicatedvia beaconing. This provides a build-out of relative location of thecontents of the first rack.

The smart device 220 then near-instantly provides a 3D rendering of theclosest rack and its contents using 3D rendering service 230. In someembodiments, the rendering service 230 is resident on the smart device220, while in other embodiments, service 230 is fully or partiallyremote from the smart device 220. This 3D rendering can then be expandedwithin the mesh overlay to build out model 226 of the remaining assetsin the data center. The first rack build-out only focuses on Y axisrelative location data (“higher” or “lower”) but with X axis proximityand the smart device measurement data, inference of assets in adjacentracks can be discovered. This process is repeated until all assets inthe data center are discovered and modeled, and data center 3D map 234is completed. Note that map 234 can be presented on the smart device 220and/or on some other computer system. Note also that the renderingservice 230 can be part of the smart device 220, remote from the smartdevice 220, or some combination thereof. Also, in alternativeembodiments, model generation can be fully or partially remote from thesmart device 220.

It is to be appreciated that the 3D map generated using architecture ofFIG. 2 is one example of a representation of assets in a data center onwhich metadata overlays generated in accordance with architecture 100 ofFIG. 1 are superimposed. Thus, techniques for generating one or moremetadata overlays on a three-dimensional representation of a computingenvironment are not limited to a map generated in accordance with theFIG. 2 embodiment.

In one illustrative embodiment, topology service 114 generates atopology of a data center. That is, FIG. 3 depicts an architecture for atopology service for a computing environment (e.g., 114), according toan embodiment of the invention. The topology service 114 providesautomated infrastructure management in a computing environment. Moreparticularly, illustrative embodiments leverage graph theory concepts,semantics, and algorithms, modeling multi-domain infrastructure systemsas vertices and edges in a graphical representation. Such graphs presentinformation in a format that is query-able and provide a more completeview of the state of a data center in order to improve the efficiencywhen performing management operations. This representation of a datacenter also enables the application of graph algorithms across domainsand provides an efficient way to calculate costs in a data center.

In one or more illustrative embodiments, disaggregated monitoringservices are deployed across the data center to feed the overall graphstate. Both temporal and spatial data points are collected by theservices to provide a near consistent view of the entire data centerinfrastructure at any given point in time. Observability is paramount tothe overall graph quality. In one or more illustrative embodiments, whenreferring to temporal data points versus spatial data points, it is adifferentiation between time-series data (things that change over timeand thus are considered temporal) versus inventory data (things that donot change or change infrequently and thus are considered spatial).Sample temporal data may include, but is not limited to, data pointscomprising: performance data/metrics (e.g., utilization, per secondmeasurements, etc.); power utilization data; monitoring data (e.g.,voltage, temperature, error states). Sample spatial data may include,but is not limited to, data points comprising: location data (e.g.,geographic with latitude/longitude to enable polar coordinatecalculations); serial numbers; part numbers; inventory data (e.g., childdevices, current software versions, etc.); state inventory data (e.g.,current operating system, current applications—this could be temporal insome instances).

A superset graph (super-graph) is maintained that is a composition ofindividual site sub-graphs. The individual site sub-graphs focus onlower layers of infrastructure to the Open Systems Interconnection (OSI)Network Model (primarily layer 1 & layer 2). Layers 3+ are maintainedwithin the superset graph which facilitates multi-data centerdeployments and stretched cluster concepts with minimal effort beyond anobserver that links the native technology to specific spatialboundaries.

Further, in terms of temporal data sets, utilization data is collectedand assigned to all vertices and edges within the context of thattechnology space. The data itself is aggregated and summarized intocross-domain weights that facilitate contextual traversals based onhigher-order use cases. For example, in one or more embodiments,cross-domain weight assignment is an aggregate value determined fromcompressing all graph layers into one view. A sample graphing algorithmis A* or Djikstra's Algorithm which does path cost minimization across atwo-dimensional graph. Performance of such algorithms includes acompression of layers into these cross-domain weights and then allowinga sub-graph traversal to occur. The weight isarbitrary/implementation-specific but the feeders are expressed below.An example would be how to effectively highlight limiting factors. Anetwork connected storage device may report a utilization of 50% but theunderlying network connection may report a utilization of 100%. Thecross-domain utilization report would be 100% since the dependency wouldbe discernable in the graph and the limiting factor could be presentedfor a raw performance scenario.

This infrastructure monitoring combined with contextual relationshipdata in graph format enables powerful queries and traversal mechanisms.

Additional layering of the data with application context providesinstant answers to the ramifications of network outages and evendetermine potential redundancy concerns within a data center. Theoverall query power for infrastructure becomes practically limitless.

Still further, in one or more illustrative embodiments, during graphqueries and traversals, ideal subgraphs are near instantly generated viaexisting storage snapshot technologies to ensure a consistent state ofthe infrastructure. Illustrative embodiments also align a subgraphlocking mechanism to fence (or lock) infrastructure from change duringquery and action steps.

FIG. 3 is a block diagram of a computing environment with automatedinfrastructure management functionality in a computing environment,according to an embodiment of the invention. As shown, a first datacenter 302-1 is operatively coupled to a second datacenter 302-2. Thetwo data centers 302-1 and 302-2 are coupled by inter data centernetworking 340. Note that in relation to the data center referenced inFIG. 1, the computing environment in FIG. 3 is intended to illustrate atopology service that can be applied to the data center of FIG. 1through topology service 114. Thus, it can be assumed that anydescription and components mentioned in the context of the datacenter(s) in FIG. 3 are present in illustrative embodiments of the datacenter in FIG. 3. For example, assets 124 in FIG. 1 are similarlyconfigured as assets to be discussed below in the context of FIG. 3.

The infrastructure of the first data center 302-1 comprises three racks310-1 (Rack 1), 310-2 (Rack 2) and 310-3 (Rack 3). Each rack comprisesone or more network devices 314, one or more server devices 316 and oneor more storage appliances 318. Compute, storage and network components(assets) 314, 316, 318 from each rack report to a monitoring service 320(320-1 in Rack 1, 320-2 in Rack 2 and 320-3 in Rack 3). Each monitoringservice 320 sends data to a graph generation engine 322-1. Graphgeneration engine 322-1 generates a graph 350-1 from the infrastructuremonitoring data received from the various monitoring services 320-1,320-2 and 320-3. For example, within graph 350-1 (which may beconsidered a relative flattened topology view), each compute, storageand network component 314, 316, 318 is represented by a vertex 351wherein two vertices are interconnected by an edge 352. Each rack 310-1,310-2 and 310-3 is represented as a larger circle 353. Graph generationengine 322-1 synchronizes with an infrastructure super-graph generationengine 330. By synchronize, in illustrative embodiments, it is meantthat the graph generation engine 322-1 provides the sub-graph generatedlocally at the data center 302-1 to infrastructure super-graphgeneration engine 330.

Similarly, the infrastructure of the second data center 302-2 comprisesthree racks 310-4 (Rack 4), 310-5 (Rack 5) and 310-6 (Rack 6). Each rackcomprises one or more network devices 314, one or more server devices316 and one or more storage appliances 318. Compute, storage and networkcomponents 314, 316, 318 from each rack report to a monitoring service320 (320-4 in Rack 4, 320-5 in Rack 5 and 320-6 in Rack 6). Eachmonitoring service 320 sends data to a graph generation engine 322-2.Graph generation engine 322-2 generates a graph 350-2 from theinfrastructure monitoring data received from the various monitoringservices 320-4, 320-5 and 320-6. The vertices and edges in graph 350-2(which may be considered a relative flattened topology view) representthe components in the second data center 302-2 in a similar manner asexplained above for graph 350-1. Graph generation engine 322-2synchronizes with the infrastructure super-graph generation engine 330.By synchronize, in illustrative embodiments, it is meant that the graphgeneration engine 322-2 provides the sub-graph generated locally at thedata center 302-1 to infrastructure super-graph generation engine 330.

Note that 350-1 and 350-2 are subgraphs and together (as depicted inFIG. 3) they represent a flattened topology super-graph 360 generatedfrom the subgraphs by infrastructure super-graph generation engine 330.

Note that, in some embodiments, infrastructure super-graph generationengine 330 can be implemented in its own computing device remote fromeither data center 302-1 or 302-2, and in other embodiments, it can beimplemented in one of the data centers or across both data centers. Notefurther that the infrastructure super-graph generation engine 330 ispart of a multi-domain infrastructure manager 335 in illustrativeembodiments.

Note also that while FIG. 3 shows only two data centers and only threeinfrastructure racks in each data center, it is to be understood thatthis is for illustrative purposes and thus embodiments are not solimited and apply to computing environments with more or fewer datacenters and racks/components therein.

As mentioned above, monitoring services 320 are disaggregated monitoringservices deployed across the computing environment to feed the overallgraph state. Both temporal and spatial data points are collected by themonitoring services 320 from the compute, storage and network components(314, 316 and 318) to provide a near consistent view of the given datacenter at any given point in time. Infrastructure super-graph generationengine 330 obtains synchronized data from each graph generation engine322 to generate and maintain a super-graph that is a composition ofindividual data center sub-graphs. In some embodiments, the individualdata center sub-graphs focus primarily on OSI layer 1 and layer 2, whileOSI layers 3+ are maintained within the superset graph. Further, asmentioned above, utilization data is collected and assigned to allvertices 351 and edges 352 within the context of that technology space.The data itself is aggregated and summarized into cross-domain weightsthat facilitate contextual traversals based on higher-order use cases.

As mentioned above, infrastructure monitoring combined with contextualrelationship data in graph format enables powerful queries and traversalmechanisms. Thus, for example, an IT operator or system queriesinfrastructure super-graph generation engine 330 to access one or moregraphs and to effectuate one or more graph operations.

In illustrative embodiments, topology service 114 is utilized as acorrelation engine wherein the full set of asset data is gathered fromthe beacons, then the topology discovery service is used to identify theavailability of assets within the domain and 3D mapping service 110 isused for non-transient data such as platform type. Through the topologyservice 114, a determination is made as to which assets have whichplatforms and therefore the call made into the platform and clusterinterface is optimized. The topology service acts as supplemental data.

FIGS. 4A-4I depict an example of building a 3D map with metadataoverlays for a portion of a data center. FIG. 4A shows an image 400 of aclosed physical data center rack. The image 400 may be captured by thesmart device 220 using image capture module 222. FIG. 4B illustrates animage 402, which shows the closed physical data center rack from image400 as well as three additional racks 404 that are populated. Usingbroadcasting/beaconing technology such as Bluetooth, it is determinedthat additional assets exist in the data center shown in image 400. Inthis case, it is assumed that such additional assets are racks andservers. The image 400 may thus be augmented as shown in FIG. 4B. Thus,the image in FIG. 4B is an augmented image 402, where three additionalracks 404 are populated in the image 400. Each of the additional assetsdetected using the broadcasting/beaconing technology is represented as abox in one of the three additional racks 404.

Secondary sources may be used to correlate assets to a type of server orother hardware that the asset represents. In FIG. 4C, it is determinedthat a particular asset 406 (e.g., represented as a 3D rectangle) may becorrelated to a particular type of server using the techniques describedherein. An image 408 of that server may be obtained from one or moresecondary sources. The image 408 may then be used to build up a physicalrendering of a rack 410 (e.g., one of the racks 404 in image 402,assuming that each asset in that rack corresponds to an instance of theserver represented by image 408). It should be noted that obtainingsource images of an asset is not a requirement. If a particular assetdoes not have a corresponding source image available, for instance, thatasset may be represented as a 3D rectangle or other placeholdervisualization of an asset as desired. FIG. 4D shows an image 412 of thethree racks 404 populated in FIG. 4B. In this example, it is assumedthat each of the three racks are built out with the same server type,and thus each of the 3D rectangles in the three racks 404 may bereplaced with a secondary source image to result in the image 414 shownin FIG. 4E. The image 414 shows a fully populated rendering of the threeracks 404. The image 414 may be projected onto a Network OperationsCenter (NOC) wall or otherwise utilized for 3D mapping of at least aportion of a data center.

Assuming image 414 in FIG. 4E is a 3D map of a portion of a data center,image 416 in FIG. 4F represents an example of data center 3D map withmetadata overlays 130 from FIG. 1. That is, metadata overlays (graphics)collectively denoted as 418 are superimposed over the assets in theracks in the 3D map of image 416. As mentioned, image 416 can bedisplayed on an IT administrator's smart device or on the wall of thedata center.

FIG. 4G illustrates a view 420 of the data center 3D map with metadataoverlay on, for example, the screen of the IT administrator's smartdevice. The smart device is configured to enable the IT administrator toactivate a pulldown menu 422 of action options. In this example, actionssuch as Shutdown, Startup, and Offload are depicted. Additional and/oralternative actions can be displayed in the menu 422. Assume the userselects Shutdown as the action to be visualized (e.g., by clicking on orotherwise activating the button representing the desired action option).Now as shown in view 424 in FIG. 4H, the IT administrator can select anasset and/or component to which the action is to be applied. Forexample, selection can be made by the IT administrator swiping left(represented by arrow 426), and thus highlighting the asset (e.g.,server) and/or component (e.g., POD1 running on the server) to beimpacted by the proposed shutdown. Then, in view 428 of FIG. 4I, theimpact to that asset/component is visually animated as part of anadditional metadata overlay 430. The IT administrator can then confirmor cancel the action using selectable buttons 432 as described below.For example, as shown, if the asset in the leftmost equipment rack ispowered off, then POD1 will be automatically moved to be hosted on anasset within the next rack over (recall that a hypervisor or othervirtualization manager can be configured to move a virtual componentfrom one physical asset to another physical asset based on the ITadministrator setting an asset state change, e.g., shutdown). Thus, the3D map and metadata overlays give the IT administrator a projectedimpact that the action he/she seeks to perform will have on the datacenter. It is to be understood that the overlays shown in FIGS. 4Fthrough 4I are intended to be examples only, and that additional and/oralternative overlays can be generated and superimposed on a data center3D image in other embodiments.

In the context of FIG. 1, note that the proposed action to be visualizedis selected on the screen of the smart device of the IT administrator orother user whereby the screen is represented as part of 130 in FIG. 1.Once the user performs a selection action such as swiping left on anasset, as mentioned above, a function call is returned through the VRcontroller interface 102 to the resource controller 104 to the platformand cluster interfaces 116/118. Metadata is then returned back throughthe resource controller 106 which uses the 3D mapping service 110,secondary sources 112 and/or topology service 114 to generate one ormore overlays, as described herein. The metadata overlays are sent tothe VR controller interface 102, and up to the data center 3D map 130for display. The user will thus see the animation (e.g., 430 in FIG.4I). The user will then be prompted to either “Confirm” action or“Cancel” action as per respective selectable buttons 432 (see FIG. 4I)displayed on the screen of the smart device. Confirm action will performa second set of calls through the above described workflow which willinitiate the migration. Cancel will reset the VR visualization to a“current view”, thus removing the asset animation projection overlay430.

Turning now to FIGS. 5A-5C, a methodology for generation of athree-dimensional map of a computing environment and associated workflowimpact overlays is depicted, according to an embodiment of theinvention. It is to be appreciated that given the metadata overlaygeneration functionalities described above, the methodology of FIGS.5A-5C is one example of an operational flow that may be performed by thesystem 100 in FIG. 1 in accordance with an illustrative embodiment.

In step 500, a smart device initiates a display request in a locationwhich hosts assets.

In step 502, software determines point 0,0 on rack 0 based on currentlocation.

In step 504, the VR controller interface 102 performs a call into theresource controller 104 with the location.

In step 506, resource controller 104 calls interface mapping module(request service) 106.

In step 508, interface mapping module 106 calls 3D mapping service 110,wherein the location data is mapped to existing 3D mapping data toindicate asset set to return. In the instance of a rack-based system,all assets within a rack are returned, their relative position and 3Doverlay of assets is generated.

In step 510, interface mapping module 106 calls secondary source(s) 112to verify that assets are in working health. Note that beacon data cannot return feedback on aspects such as power or health, so correlationservices from secondary source(s) 112 are useful to indicate theexisting but “powered off” assets. This step is ongoing wherein updatesare displayed in real-time.

In step 512, interface mapping module 106 calls to topology service 114,platform interfaces 116 and cluster interfaces 118 with an asset displaylist. Platform and cluster interfaces 116/118 return workload tointerface mapping module 106. This step is also ongoing wherein updatesare displayed in real-time.

In step 514, interface mapping module 106 aggregates all returned datapoints from 110, 112, 114, 116 and 118 and returns them to VR controllerinterface 102. This step is also ongoing wherein updates are displayedin real-time.

In step 516, VR controller interface 102 uses an image library to createone image per workload, these are added on the asset-specific display.This step is also ongoing wherein updates are displayed in real-time.

In step 518, the IT administrator indicates an asset will need action.This is done through a physical tap or swipe on the smart device.

In step 520, VR controller interface 102 sends an action message toresource controller 106.

In step 522, resource controller 106 sends a “read only” call to datacenter workflow module 108, and data center workflow module 108 returnsa possible impact of the action.

In step 524, VR controller interface 102 animates the possible impact byanimating any workloads on the impacted asset. The workloads areanimated through straightforward VR controller interface capabilitiesfrom their starting location to their possible new location (datareturned in previous step).

In step 526, the user is shown the possible animation. This is donethrough the VR controller interface 102 which is given the starting andending point of the objects/workloads using one or more standardanimation libraries.

In step 528, the user is prompted to “confirm” (and cause the action tooccur) or to “cancel” (an cause the action not to occur).

In step 530, user-selection of “cancel” resets the VR visualization tothe “current view” and remove the asset animation projection overlay.

In step 532, user-selection of “confirm” sends an action call from theVR controller interface 102 to the resource controller 104, whichinitiates the data center workflow module 108, which makes action callsto platform interfaces 116 and cluster interfaces 118 as needed.

In step 534, continuous monitoring per the above description returnsmodified data center information back up through the VR controllerinterface 102 which updates the data center map with metadata overlayinformation.

Use Case Example

Assume that, post installation, the review of a rack requires re-cablingto meet data center standards.

Prior to completing the re-cabling, the data center administrator isable to use the data center 3D map with metadata overlays generationtechniques described herein, and by looking at their smart device to seewhat is running on a given rack.

The administrator could then choose the “project changes” optionavailable on their smart device and choose to power down the servers onthe rack as the projected change.

On the smart device of their choice, they will see an overlay of how theworkloads would migrate. If they are using a smartphone, outlines orhotspot indicators would show that the workload running on rack1,server2 will be physically moved to rack2, server 1 in a drawing on thescreen that overlays over the physical space. If using projection AR,the images that are projected onto Rackl, server2 will show a projectedmovement of the workloads to rack2, server1.

If the available platforms would become overloaded and if anyapplications would be left without a location to migrate to, theworkloads would show as ‘homeless’ and an indication of the error asrepresented by the secondary data source (in this instance, VMwareVrealizeOps).

IT personnel can make intelligent decisions based on what would beaffected if they re-cabled the rack to meet the standards.

FIG. 6 illustrates a processing platform used to implement anarchitecture for generating maps with metadata overlays for a computingenvironment, according to an embodiment of the invention. Moreparticularly, processing platform 600 is a processing platform on whicha computing environment with improved map generation functionalities(e.g., FIGS. 1-5C and otherwise described herein) can be implemented.

The processing platform 600 in this embodiment comprises a plurality ofprocessing devices, denoted 602-1, 602-2, 602-3, . . . 602-N, whichcommunicate with one another over network(s) 604. It is to beappreciated that the methodologies described herein may be executed inone such processing device 602, or executed in a distributed manneracross two or more such processing devices 602. It is to be furtherappreciated that a server, a client device, a computing device or anyother processing platform element may be viewed as an example of what ismore generally referred to herein as a “processing device.” Asillustrated in FIG. 6, such a device generally comprises at least oneprocessor and an associated memory, and implements one or morefunctional modules for instantiating and/or controlling features ofsystems and methodologies described herein. Multiple elements or modulesmay be implemented by a single processing device in a given embodiment.Note that components described in the architecture 100 of FIG. 1 cancomprise one or more of such processing devices 602 shown in FIG. 6. Thenetwork(s) 604 represent one or more communications networks that enablecomponents to communicate and to transfer data therebetween, as well asto perform other functionalities described herein.

The processing device 602-1 in the processing platform 600 comprises aprocessor 610 coupled to a memory 612. The processor 610 may comprise amicroprocessor, a microcontroller, an application-specific integratedcircuit (ASIC), a field programmable gate array (FPGA) or other type ofprocessing circuitry, as well as portions or combinations of suchcircuitry elements. Components of systems as disclosed herein can beimplemented at least in part in the form of one or more softwareprograms stored in memory and executed by a processor of a processingdevice such as processor 610. Memory 612 (or other storage device)having such program code embodied therein is an example of what is moregenerally referred to herein as a processor-readable storage medium.Articles of manufacture comprising such processor-readable storage mediaare considered embodiments of the invention. A given such article ofmanufacture may comprise, for example, a storage device such as astorage disk, a storage array or an integrated circuit containingmemory. The term “article of manufacture” as used herein should beunderstood to exclude transitory, propagating signals.

Furthermore, memory 612 may comprise electronic memory such asrandom-access memory (RAM), read-only memory (ROM) or other types ofmemory, in any combination. The one or more software programs whenexecuted by a processing device such as the processing device 602-1causes the device to perform functions associated with one or more ofthe components/steps of system/methodologies in FIGS. 1-4. One skilledin the art would be readily able to implement such software given theteachings provided herein. Other examples of processor-readable storagemedia embodying embodiments of the invention may include, for example,optical or magnetic disks.

Processing device 602-1 also includes network interface circuitry 614,which is used to interface the device with the networks 604 and othersystem components. Such circuitry may comprise conventional transceiversof a type well known in the art.

The other processing devices 602 (602-2, 602-3, . . . 602-N) of theprocessing platform 600 are assumed to be configured in a manner similarto that shown for computing device 602-1 in the figure.

The processing platform 600 shown in FIG. 6 may comprise additionalknown components such as batch processing systems, parallel processingsystems, physical machines, virtual machines, virtual switches, storagevolumes, etc. Again, the particular processing platform shown in thisfigure is presented by way of example only, and the system shown as 600in FIG. 6 may include additional or alternative processing platforms, aswell as numerous distinct processing platforms in any combination.

Also, numerous other arrangements of servers, clients, computers,storage devices or other components are possible in processing platform600. Such components can communicate with other elements of theprocessing platform 600 over any type of network, such as a wide areanetwork (WAN), a local area network (LAN), a satellite network, atelephone or cable network, or various portions or combinations of theseand other types of networks.

Furthermore, it is to be appreciated that the processing platform 600 ofFIG. 6 can comprise virtual (logical) processing elements implementedusing a hypervisor. A hypervisor is an example of what is more generallyreferred to herein as “virtualization infrastructure.” The hypervisorruns on physical infrastructure. As such, the techniques illustrativelydescribed herein can be provided in accordance with one or more cloudservices. The cloud services thus run on respective ones of the virtualmachines under the control of the hypervisor. Processing platform 600may also include multiple hypervisors, each running on its own physicalinfrastructure. Portions of that physical infrastructure might bevirtualized.

As is known, virtual machines are logical processing elements that maybe instantiated on one or more physical processing elements (e.g.,servers, computers, processing devices). That is, a “virtual machine”generally refers to a software implementation of a machine (i.e., acomputer) that executes programs like a physical machine. Thus,different virtual machines can run different operating systems andmultiple applications on the same physical computer. Virtualization isimplemented by the hypervisor which is directly inserted on top of thecomputer hardware in order to allocate hardware resources of thephysical computer dynamically and transparently. The hypervisor affordsthe ability for multiple operating systems to run concurrently on asingle physical computer and share hardware resources with each other.

It was noted above that portions of the computing environment may beimplemented using one or more processing platforms. A given suchprocessing platform comprises at least one processing device comprisinga processor coupled to a memory, and the processing device may beimplemented at least in part utilizing one or more virtual machines,containers or other virtualization infrastructure. By way of example,such containers may be Docker containers or other types of containers.

The particular processing operations and other system functionalitydescribed in conjunction with FIGS. 1-5C are presented by way ofillustrative example only, and should not be construed as limiting thescope of the disclosure in any way. Alternative embodiments can useother types of operations and protocols. For example, the ordering ofthe steps may be varied in other embodiments, or certain steps may beperformed at least in part concurrently with one another rather thanserially. Also, one or more of the steps may be repeated periodically,or multiple instances of the methods can be performed in parallel withone another.

It should again be emphasized that the above-described embodiments ofthe invention are presented for purposes of illustration only. Manyvariations may be made in the particular arrangements shown. Forexample, although described in the context of particular system anddevice configurations, the techniques are applicable to a wide varietyof other types of data processing systems, processing devices anddistributed virtual infrastructure arrangements. In addition, anysimplifying assumptions made above in the course of describing theillustrative embodiments should also be viewed as exemplary rather thanas requirements or limitations of the invention. Numerous otheralternative embodiments within the scope of the appended claims will bereadily apparent to those skilled in the art.

What is claimed is:
 1. A method, comprising: in a computing environmentcomprising a plurality of equipment racks wherein each equipment rackcomprises one or more of compute, storage and network assets;identifying a workflow attributed to at least a portion of the assets inat least one equipment rack in the computing environment; generating oneor more overlays that visualize a projected impact to one or morecomponents of the identified workflow; obtaining a three-dimensionalrepresentation of the at least one equipment rack; and superimposing theone or more overlays on the three-dimensional representation of the atleast one equipment rack.
 2. The method of claim 1, further comprisingpresenting the three-dimensional representation with the one or moremetadata overlays to a user within the computing environment.
 3. Themethod of claim 2, wherein the presenting step is performed on a smartdevice in the possession of the user.
 4. The method of claim 2, furthercomprising, prior to the generating step, obtaining a user selectedaction for which the projected impact to one or more components of theidentified workflow pertains.
 5. The method of claim 4, wherein the oneor more overlays comprise one or more graphics representing at least aportion of the one or more components of the identified workflow.
 6. Themethod of claim 4, wherein the one or more overlays comprise one or moregraphics representing an animation associated with at least a portion ofthe one or more components of the identified workflow.
 7. The method ofclaim 6, wherein the animation visually illustrates movement of a givenone of the one or more components from a first asset to a second assetof the assets based on the projected impact.
 8. The method of claim 4,further comprising, after presenting the one or more overlays, obtaininga user confirmation to implement the selected action.
 9. The method ofclaim 1, wherein the computing environment is a data center.
 10. Asystem, comprising: at least one processor, coupled to a memory, andconfigured to: in a computing environment comprising a plurality ofequipment racks wherein each equipment rack comprises one or more ofcompute, storage and network assets; identify a workflow attributed toat least a portion of the assets in at least one equipment rack in thecomputing environment; generate one or more overlays that visualize aprojected impact to one or more components of the identified workflow;obtain a three-dimensional representation of the at least one equipmentrack; and superimpose the one or more overlays on the three-dimensionalrepresentation of the at least one equipment rack.
 11. The system ofclaim 10, wherein the at least one processor is further configured topresent the three-dimensional representation with the one or moremetadata overlays to a user within the computing environment.
 12. Thesystem of claim 11, wherein the presenting is performed on a smartdevice in the possession of the user.
 13. The system of claim 11,wherein the at least one processor is further configured to, prior tothe generating the one or more overlays, obtain a user selected actionfor which the projected impact to one or more components of theidentified workflow pertains.
 14. The system of claim 13, wherein theone or more overlays comprise one or more graphics representing at leasta portion of the one or more components of the identified workflow. 15.The system of claim 13, wherein the one or more overlays comprise one ormore graphics representing an animation associated with at least aportion of the one or more components of the identified workflow. 16.The system of claim 15, wherein the animation visually illustratesmovement of a given one of the one or more components from a first assetto a second asset of the assets based on the projected impact.
 17. Thesystem of claim 13, wherein the at least one processor is furtherconfigured to, after presenting the one or more overlays, obtain a userconfirmation to implement the selected action.
 18. The system of claim10, wherein the computing environment is a data center.
 19. An articleof manufacture comprising a processor-readable storage medium havingencoded therein executable code of one or more software programs,wherein the one or more software programs when executed by at least oneprocessing device implement steps of: in a computing environmentcomprising a plurality of equipment racks wherein each equipment rackcomprises one or more of compute, storage and network assets;identifying a workflow attributed to at least a portion of the assets inat least one equipment rack in the computing environment; generating oneor more overlays that visualize a projected impact to one or morecomponents of the identified workflow; obtaining a three-dimensionalrepresentation of the at least one equipment rack; and superimposing theone or more overlays on the three-dimensional representation of the atleast one equipment rack.
 20. The article of claim 19, furthercomprising presenting the three-dimensional representation with the oneor more metadata overlays to a user within the computing environment.